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Vicente, M., Santos, S., Gouveia, Mariana França, Pinto-Ferreira, J.P. & Seromenho, A. (2022). Pre-insolvency procedures in Portugal: A sociographic analysis of individual debtors. 7th Global Meeting on Law & Society.
M. A. Vicente et al., "Pre-insolvency procedures in Portugal: A sociographic analysis of individual debtors", in 7th Global Meeting on Law & Society, 2022
@misc{vicente2022_1734929705100, author = "Vicente, M. and Santos, S. and Gouveia, Mariana França and Pinto-Ferreira, J.P. and Seromenho, A.", title = "Pre-insolvency procedures in Portugal: A sociographic analysis of individual debtors", year = "2022", url = "https://www.lawandsociety.org/2022/05/09/global-meeting-schedule-now-available/" }
TY - CPAPER TI - Pre-insolvency procedures in Portugal: A sociographic analysis of individual debtors T2 - 7th Global Meeting on Law & Society AU - Vicente, M. AU - Santos, S. AU - Gouveia, Mariana França AU - Pinto-Ferreira, J.P. AU - Seromenho, A. PY - 2022 UR - https://www.lawandsociety.org/2022/05/09/global-meeting-schedule-now-available/ AB - In 2017, the Portuguese Parliament approved a pre-insolvency procedure (PEAP – Special Process of Payment Agreement) in order to prevent natural persons in substantial economic difficulties from defaulting and becoming bankrupted. This mechanism is very similar to a previous one dedicated to companies and collective agents - Special Revitalization Process - established in 2012, in the aftermath of the sovereign debt crisis. Both mechanisms were introduced to guarantee a preventive form - for both natural persons and companies - to renegotiate debts with their creditors and minimize the impact on others (the cascade effect). Unexpectedly, in the five years since it was approved, PEAP has been poorly used in comparison with insolvency procedures, in a 1 to 26 ratio. In this paper we present the data collected from the IN_SOLVENS project, the first research project in Portugal to analyze PEAP court filings from 2017 to 2020. From the total 1579 closed court cases we have randomly selected 319, circa 20%. Our sample consists of a random combination of cases weighted by their number in each judicial district. We will use descriptive statistics and multiple correspondence analysis to present different profiles combining gender, age, income, professional situation, marital status, type of debts (social security, fiscal, credit card, house mortgage), type of creditors (state, banks, other financial institutions) and corresponding debt values. ER -